Improved understanding of disease mechanisms and drug target identification requires better understanding of how diseases alter cellular processes from the healthy state. Our group has developed an integrative pathway search algorithm that reconstructs networks of active pathways from gene expression and phenotypic profiles. Preliminary studies illustrate that this framework is able to reconstruct networks that include those pathways that should be altered, and how they should be altered, to obtain a desired phenotype. Nevertheless, the current framework may not fully capture transients, such as cycles and feedback loops. Furthermore, cells continuously reprogram gene regulatory networks as they sense changes in their environment. To understand how cells are regulated in response to environmental alterations, time series (i.e., dynamic) data are required. Correspondingly, a dynamic model is required to uncover the mechanisms from time series data. We hypothesize that incorporating domain knowledge and metabolic data into a dynamic model would enhance the accuracy of the genes chosen and in turn improve the prediction of the reconstructed networks. Unlike previous studies that have focused on using the gene ontology information, we propose to incorporate domain knowledge retrieved from the free text. This is significant because a large portion of the genes do not have gene ontological keywords. Additionally, it is often difficult to assess the accuracy of the network structures that have been inferred from experimental data because the underlying "true" regulatory network is unknown or unavailable a priori. Therefore, one needs to have a known network structure that can be used to optimize and evaluate the modeling frameworks. Once so optimized, the static and dynamic models will be applied to an experimental cell culture system, which has a perturbed (transfected or silenced) gene, and assessed as to how well each model predicts the resulting, measured phenotypic responses. The cost-effectiveness of the cell culture, in contrast to in vivo animal studies, allows us to establish, with experimental data, which model produces predictions of greater confidence. Having established which model is more predictive, we will apply that model to rats that are maintained on high fat diets, so as to identify the pathways that could be altered to reduce triglyceride storage (steatosis) and inflammation in the livers of these rats. The findings could have implications for identifying potential therapies for steatosis and, perhaps, even non-alcoholic steatohepatitis (NASH). The objectives will be achieved through the following aims: 1) Develop a novel approach that incorporates domain knowledge retrieved from the free text as well as gene expression data to predict cellular or phenotypic responses. 2) Develop an optimized dynamic Bayesian Network to infer gene regulatory networks from time series data. 3) Experimentally validate the model predictions for the cell culture system. 4) Characterize the livers from rats fed high fat vs. normal diets.